Elevation of CD40/CD40L Inflammatory Pathway Molecules in Carotid Plaques from Moderate-and-Severe Obstructive Sleep Apnea Patients

Migacz, Ewa; Olejarz, Wioletta; Głuszko, Alicja; Bednarek-Rajewska, Katarzyna; Proczka, Robert; Dl, Smith; Ishman, Stacey L.; Kukwa, Wojciech

doi:10.3390/diagnostics11060935

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…In this study, we assessed the importance of 24 features to OSAHS, and we were surprised to nd that LDL-C, Cr, carotid plaque, A1c and BMI made major contributions to OSAHS. Studies have shown an increased prevalence of OSAHS in patients with metabolomics, chronic kidney disease, carotid plaques, diabetes and obesity (25)(26)(27)(28)(29), which is consistent with our ndings. Importantly, we evaluated the ability of ML models created by ve algorithms to distinguish between OSAHS and non-OSAHS diseases, namely the commonly used Random Forest, boosting, SVM, and logistic regression and MLP methods.…”

Section: Discussionsupporting

confidence: 93%

Machine Learning Methods for Adult OSAHS Risk Prediction

Ge,

Wu,

et al. 2024

Preprint

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Background Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common disease that can cause multiple organ damage in the whole body. Our aim was to use machine learning (ML) to build an independent polysomnography (PSG) model to analyze risk factors and predict OSAHS. Materials and Methods Clinical data of 2064 snoring patients who underwent physical examination in the Health Management Center of the First Affiliated Hospital of Shanxi Medical University from July 2018 to July 2023 were retrospectively collected, involving 24 characteristic variables. Then they were randomly divided into training group and verification group according to the ratio of 7:3. By analyzing the importance of these features, it was concluded that LDL-C, Cr, common carotid artery plaque, A1c and BMI made major contributions to OSAHS. Moreover, five kinds of machine learning algorithm models such as logistic regression, support vector machine, Boosting, Random Forest and MLP were further established, and cross validation was used to adjust the model hyperparameters to determine the final prediction model. We compared the accuracy, Precision, Recall rate, F1-score and AUC indexes of the model, and finally obtained that MLP was the optimal model with an accuracy of 85.80%, Precision of 0.89, Recall of 0.75, F1-score of 0.82, and AUC of 0.938. Conclusion We established the risk prediction model of OSAHS using ML method, and proved that the MLP model performed best among the five ML models. This predictive model helps to identify patients with OSAHS and provide early, personalized diagnosis and treatment options.

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Section: Discussionsupporting

confidence: 93%

Machine Learning Methods for Adult OSAHS Risk Prediction

Ge,

Wu,

et al. 2024

Preprint

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“…The severity of OSA was significantly associated only with MCP-1. The CD40 was nearly significant ( p <0.056), suggesting that the CD40/CD40L inflammatory pathway together with MCP-1 may contribute to plaque instability and rupture in OSA ( 43 ).…”

Section: Discussionmentioning

confidence: 99%

Markers of Carotid Plaque Destabilization in Patients With Sleep-Disordered Breathing

2022

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Sleep-disordered breathing (SDB) is a nightly respiratory condition characterized by intermittent hypoxia, leading to oxidative stress, inflammation, and atherosclerosis. However, most cellular markers of human carotid plaques in SDB have not yet been assessed. We aimed at characterizing the cellular, inflammatory, and nitro-oxidative stress markers in carotid plaques obtained from 25 patients undergoing endarterectomy and screened for SDB. Sleep studies were performed during their preoperative hospitalization night using the Watch-PAT 100 device. Oxygen desaturation index (ODI) was used for dividing patients into two groups. Fourteen patients with ODI >5 were designated as SDB and 11 patients with ODI ≤ 5 as non-SDB. Demographics, comorbidities, cardiovascular risk factors, and medications were recorded. Cellular markers in plaques were analyzed by immunofluorescence using confocal microscopy. The expression of neutrophils was identified by CD66b+ and neutrophil elastase, macrophage-foam cells were identified by CD163+, and scavenger receptors by CD68+ and CD36+ expression. Additional markers included 3-nitrotyrosine, endothelial CD31, and smooth muscle cell-actin (SMC-actin). Plaques' lipids were determined by immunohistochemistry with Oil Red O staining. Notably, significantly higher values were found for SDB as compared to patients with non-SDB for 3-nitrotyrosine (p <0.004) and intracellular lipids' content (p <0.02), whereas SMC-actin was lower (p <0.006). There were no significant differences between patients with carotid-associated symptoms (symptomatic) and patients without carotid-associated symptoms (asymptomatic). However, a sub-group of symptomatic patients with co-existent SDB expressed the highest 3-nitrotyrosin, and intracellular lipids levels, and the lowest SMC-actin levels, whereas non-SDB/asymptomatic patients expressed the lowest 3-nitrotyrosin and lipids levels and the highest SMS-actin levels among all patients. Accordingly, ODI was lowest in non-SDB/asymptomatic patients and highest in SDB/symptomatic. In conclusion, plaques of patients with SDB were characterized by markedly increased levels of 3-nitrotyrosine and intracellular lipids content. Conversely, SMC-actin levels were significantly lower. These three markers, such as increased 3-nitrotyrosine and intracellular lipids and decreased SMC-actin are associated with plaque vulnerability and instability. These findings are in line with earlier reports demonstrating increased intima-media thickness in large cohorts of sleep apnea and patients with SDB, and thus, may indicate a higher susceptibility to plaque vulnerability and rapture in patients with SDB.

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“…One of inflammatory pathway, CD40-CD40L, enhances leukocyte recruitment to the sites of vascular inflammation and stimulates macrophages to produce matrix metalloproteinase (MMP). This process is associated with atherosclerotic plaque formation ( Li et al, 2020 ; Migacz et al, 2021 ). Fourth, Cys-C is also known as neutrophil growth factor and is involved in the migration, phagocytosis, and inflammation of neutrophils.…”

Section: Discussionmentioning

confidence: 99%

Association Cystatin C and Risk of Stroke in Elderly Patients With Obstructive Sleep Apnea: A Prospective Cohort Study

Gao

et al. 2021

Front. Neurosci.

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Background: Few prospective cohort studies have assessed the relationship between Cystatin C (Cys-C) and risk of stroke in elderly patients with obstructive sleep apnea (OSA). The study sought to examine the association between baseline serum Cys-C and long-term risk of stroke among elderly OSA patients.Methods: A total of 932 patients with OSA, no history of stroke, ≥60 years of age, and complete serum Cys-C records were included in this study. All patients had completed polysomnography (PSG). OSA was defined as an apnea-hypopnea index (AHI) of ≥5 events per hour. Participants were categorized into four groups according to baseline serum Cys-C concentration, split into quartiles. Multivariate Cox regression were used to evaluate the association between Cys-C and the incidence of new-onset stroke.Results: Stroke occurred in 61 patients during the median 42-month follow-up period. The cumulative incidence rate of stroke was 6.5%, which included 54 patients with ischemic stroke and 7 patients with hemorrhagic stroke. The cumulative incidence of stroke was higher among patients with baseline serum Cys-C concentration of ≥1.15 mg/L when compared with other groups (PLog–rank < 0.001). After adjusting for potential confounding factors in the Cox regression model, patients with a serum Cys-C concentration of ≥1.15 mg/L had a 2.16-fold higher risk of developing stroke compared with patients with serum Cys-C ≤ 0.81 mg/L (HR, 2.16, 95%CI, 1.09–6.60; P = 0.017). Additionally, there was a higher risk in those of age ≥70 years (HR, 3.23, 95%CI, 1.05–9.24; P = 0.010). The receiver-operating characteristic curves showed that the capability of Cys-C to identify elderly patients with OSA who had a long-time risk of stroke was moderate (AUC = 0.731, 95% CI: 0.683–0.779, P = 0.001).Conclusion: Increased Cys-C concentration was identified as a risk factor in the incidence of stroke in elderly patients with OSA, independent of gender, BMI, hypertension and other risk factors. Additionally, it conferred a higher risk in patients of age ≥70 years.

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Elevation of CD40/CD40L Inflammatory Pathway Molecules in Carotid Plaques from Moderate-and-Severe Obstructive Sleep Apnea Patients

Cited by 4 publications

References 36 publications

Machine Learning Methods for Adult OSAHS Risk Prediction

Machine Learning Methods for Adult OSAHS Risk Prediction

Markers of Carotid Plaque Destabilization in Patients With Sleep-Disordered Breathing

Association Cystatin C and Risk of Stroke in Elderly Patients With Obstructive Sleep Apnea: A Prospective Cohort Study

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